Analysis and Comparison of Common Mode Error Extraction Using Principal Component Analysis and Independent Component Analysis" />
<div style="line-height: 150%">Analysis and Comparison of Common Mode Error Extraction Using Principal Component Analysis and Independent Component Analysis</div>
Abstract Common mode error(CME), a major source of error correlated spatially in regional GPS solutions, should be removed to enhance signal-to-noise ratio in GPS coordinate time series. Principal component analysis (PCA), which is widely used for CME extraction, decomposes the time series of the GPS network into a group of modes, where each mode consists of a common temporal function and corresponding spatial response based on second-order statistics. Since the probability distribution function of GPS time series is sometimes no-Gaussian, the second-order statistic cannot fully capture its stochastic characteristics. In this paper, we assume that CME is stochastic independent with other error sources, so an independent component analysis (ICA) is introduced to analyze it. The performance of ICA is validated and compared with that of PCA through a simulated example.
